{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f2faf02-2812-48cc-a446-20c51ce53db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python/3.10.13/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f6cf20a-f641-4f58-8946-92171476c2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('documents-with-ids.json', 'rt') as f_in:\n",
    "    documents = json.load(f_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91fab490-d35b-47cc-94ce-d46194274457",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python/3.10.13/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model_name = 'multi-qa-MiniLM-L6-cos-v1'\n",
    "model = SentenceTransformer(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d63b5a43-0344-43e3-90af-48e0c105dd0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [01:55<00:00,  8.19it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    question = doc['question']\n",
    "    text = doc['text']\n",
    "    qt = question + ' ' + text\n",
    "\n",
    "    doc['question_vector'] = model.encode(question)\n",
    "    doc['text_vector'] = model.encode(text)\n",
    "    doc['question_text_vector'] = model.encode(qt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "507be6a7-f937-4e41-81f3-d49e942ac3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200') \n",
    "\n",
    "index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"section\": {\"type\": \"text\"},\n",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"},\n",
    "            \"id\": {\"type\": \"keyword\"},\n",
    "            \"question_vector\": {\n",
    "                \"type\": \"dense_vector\",\n",
    "                \"dims\": 384,\n",
    "                \"index\": True,\n",
    "                \"similarity\": \"cosine\"\n",
    "            },\n",
    "            \"text_vector\": {\n",
    "                \"type\": \"dense_vector\",\n",
    "                \"dims\": 384,\n",
    "                \"index\": True,\n",
    "                \"similarity\": \"cosine\"\n",
    "            },\n",
    "            \"question_text_vector\": {\n",
    "                \"type\": \"dense_vector\",\n",
    "                \"dims\": 384,\n",
    "                \"index\": True,\n",
    "                \"similarity\": \"cosine\"\n",
    "            },\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"course-questions\"\n",
    "\n",
    "es_client.indices.delete(index=index_name, ignore_unavailable=True)\n",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1e8ecc4-f726-4e9a-b648-0c9226743b6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 948/948 [00:26<00:00, 36.07it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e7dcba-7ca8-4a69-9caa-e01571b8d76c",
   "metadata": {},
   "source": [
    "## Hybrid search example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e3a97cf-dc8a-4019-bf3a-ea29616c2065",
   "metadata": {},
   "outputs": [],
   "source": [
    "course = \"data-engineering-zoomcamp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ef601f8-ffe0-4bdf-b3f6-e9dc765b2c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I just discovered the course. Can I still join it?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53dd190c-86d4-4d4a-a374-4075542094bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "v_q = model.encode(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d1547eb0-789a-43ee-a211-63719528e221",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_query = {\n",
    "    \"field\": \"text_vector\",\n",
    "    \"query_vector\": v_q,\n",
    "    \"k\": 5,\n",
    "    \"num_candidates\": 10000,\n",
    "    \"boost\": 0.5,\n",
    "    \"filter\": {\n",
    "        \"term\": {\n",
    "            \"course\": course\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c0ed651c-130c-40b5-bbe1-e0664440ddf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword_query = {\n",
    "    \"bool\": {\n",
    "        \"must\": {\n",
    "            \"multi_match\": {\n",
    "                \"query\": query,\n",
    "                \"fields\": [\"question^3\", \"text\", \"section\"],\n",
    "                \"type\": \"best_fields\",\n",
    "                \"boost\": 0.5,\n",
    "            }\n",
    "        },\n",
    "        \"filter\": {\n",
    "            \"term\": {\n",
    "                \"course\": course\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de4ee511-1ca9-4707-bf1e-ff0400871b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = es_client.search(\n",
    "    index=index_name,\n",
    "    query=keyword_query,\n",
    "    knn=knn_query,\n",
    "    size=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3ba09df2-c55d-4f21-93ff-8de293d24dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  '_id': 'ICPvR5EBO7bpWV6zkHVw',\n",
       "  '_score': 36.424633,\n",
       "  '_source': {'text': \"Yes, even if you don't register, you're still eligible to submit the homeworks.\\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\",\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Course - Can I still join the course after the start date?',\n",
       "   'course': 'data-engineering-zoomcamp',\n",
       "   'id': '7842b56a',\n",
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       " {'_index': 'course-questions',\n",
       "  '_id': 'JSPvR5EBO7bpWV6zkXU7',\n",
       "  '_score': 27.396076,\n",
       "  '_source': {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Course - Can I follow the course after it finishes?',\n",
       "   'course': 'data-engineering-zoomcamp',\n",
       "   'id': 'a482086d',\n",
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       " {'_index': 'course-questions',\n",
       "  '_id': 'IiPvR5EBO7bpWV6zkHXB',\n",
       "  '_score': 21.920742,\n",
       "  '_source': {'text': 'You can start by installing and setting up all the dependencies and requirements:\\nGoogle cloud account\\nGoogle Cloud SDK\\nPython 3 (installed with Anaconda)\\nTerraform\\nGit\\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.',\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Course - What can I do before the course starts?',\n",
       "   'course': 'data-engineering-zoomcamp',\n",
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       " {'_index': 'course-questions',\n",
       "  '_id': 'JiPvR5EBO7bpWV6zkXVu',\n",
       "  '_score': 21.325657,\n",
       "  '_source': {'text': 'Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.',\n",
       "   'section': 'General course-related questions',\n",
       "   'question': 'Course - Can I get support if I take the course in the self-paced mode?',\n",
       "   'course': 'data-engineering-zoomcamp',\n",
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       "  '_source': {'text': \"You don't need it. You're accepted. You can also just start learning and submitting homework without registering. It is not checked against any registered list. Registration is just to gauge interest before the start date.\",\n",
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       "    0.020643534138798714,\n",
       "    -0.07072151452302933,\n",
       "    -4.41106128590185e-33,\n",
       "    0.00047660607378929853,\n",
       "    -0.020899051800370216,\n",
       "    0.07973586022853851,\n",
       "    0.05646766722202301,\n",
       "    -0.0072766197845339775,\n",
       "    0.0206907968968153,\n",
       "    -0.04398543760180473,\n",
       "    0.011072726920247078,\n",
       "    -0.011190813966095448,\n",
       "    0.02080981433391571,\n",
       "    0.039939314126968384,\n",
       "    -0.09016377478837967,\n",
       "    -0.0446629524230957,\n",
       "    -0.027285335585474968,\n",
       "    0.08258123695850372,\n",
       "    0.01029777992516756,\n",
       "    0.000151514817844145,\n",
       "    -0.02362971380352974,\n",
       "    0.01609489880502224,\n",
       "    -0.055767204612493515,\n",
       "    -0.028492312878370285,\n",
       "    -0.015431826002895832,\n",
       "    -0.054363809525966644,\n",
       "    -0.016051191836595535,\n",
       "    0.053995124995708466,\n",
       "    0.055379968136548996,\n",
       "    0.039840005338191986,\n",
       "    0.045173175632953644,\n",
       "    -0.10705079883337021,\n",
       "    -0.04998397082090378,\n",
       "    -0.014069030061364174,\n",
       "    -0.004976013209670782,\n",
       "    0.016995247453451157,\n",
       "    -0.028811311349272728,\n",
       "    0.026075564324855804,\n",
       "    0.024125825613737106,\n",
       "    0.0191927719861269,\n",
       "    0.005659198388457298,\n",
       "    0.009844842366874218,\n",
       "    -0.03452266752719879,\n",
       "    -0.0004498532507568598,\n",
       "    0.09333029389381409,\n",
       "    0.0484379343688488,\n",
       "    -0.002345471875742078,\n",
       "    -0.07435981929302216,\n",
       "    0.05822377651929855,\n",
       "    -0.10094793885946274,\n",
       "    -0.054983723908662796,\n",
       "    0.016803473234176636,\n",
       "    -0.029692083597183228,\n",
       "    0.0009698471403680742,\n",
       "    -0.014921888709068298,\n",
       "    -0.004571937955915928,\n",
       "    -0.06707686185836792,\n",
       "    0.04045085981488228,\n",
       "    0.09394976496696472,\n",
       "    -0.004803560208529234,\n",
       "    -0.0273368488997221,\n",
       "    0.029812311753630638,\n",
       "    0.031071221455931664,\n",
       "    -0.037169575691223145,\n",
       "    0.028585871681571007,\n",
       "    -0.027297912165522575,\n",
       "    -0.08384589850902557]}}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response[\"hits\"][\"hits\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e805440-06c7-48e9-8a8d-feba38ace95f",
   "metadata": {},
   "source": [
    "## Hybrid search pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b7e625d-ccb3-4597-aefc-022137fc33b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ground_truth = pd.read_csv('ground-truth-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94fc36e5-7141-4a19-a445-a22a63c68471",
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth = df_ground_truth.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8bd59d82-6e4f-48b2-95ee-81031d16594b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hit_rate(relevance_total):\n",
    "    cnt = 0\n",
    "\n",
    "    for line in relevance_total:\n",
    "        if True in line:\n",
    "            cnt = cnt + 1\n",
    "\n",
    "    return cnt / len(relevance_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c3d577bb-f890-483e-9afa-580411750dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mrr(relevance_total):\n",
    "    total_score = 0.0\n",
    "\n",
    "    for line in relevance_total:\n",
    "        for rank in range(len(line)):\n",
    "            if line[rank] == True:\n",
    "                total_score = total_score + 1 / (rank + 1)\n",
    "\n",
    "    return total_score / len(relevance_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "046a7155-875b-45aa-9889-cd3410f5e86b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search_hybrid(field, query, vector, course):\n",
    "    knn_query = {\n",
    "        \"field\": field,\n",
    "        \"query_vector\": vector,\n",
    "        \"k\": 5,\n",
    "        \"num_candidates\": 10000,\n",
    "        \"boost\": 0.5,\n",
    "        \"filter\": {\n",
    "            \"term\": {\n",
    "                \"course\": course\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    keyword_query = {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": query,\n",
    "                    \"fields\": [\"question\", \"text\", \"section\"],\n",
    "                    \"type\": \"best_fields\",\n",
    "                    \"boost\": 0.5,\n",
    "                }\n",
    "            },\n",
    "            \"filter\": {\n",
    "                \"term\": {\n",
    "                    \"course\": course\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    search_query = {\n",
    "        \"knn\": knn_query,\n",
    "        \"query\": keyword_query,\n",
    "        \"size\": 5,\n",
    "        \"_source\": [\"text\", \"section\", \"question\", \"course\", \"id\"]\n",
    "    }\n",
    "\n",
    "    es_results = es_client.search(\n",
    "        index=index_name,\n",
    "        body=search_query\n",
    "    )\n",
    "    \n",
    "    result_docs = []\n",
    "    \n",
    "    for hit in es_results['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "88edfdf8-36db-4038-a259-dc1496ceedcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_hybrid(q):\n",
    "    question = q['question']\n",
    "    course = q['course']\n",
    "\n",
    "    v_q = model.encode(question)\n",
    "\n",
    "    return elastic_search_hybrid('question_vector', question, v_q, course)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "231738e6-fb84-42f6-bc28-f4cbbdcfabdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(ground_truth, search_function):\n",
    "    relevance_total = []\n",
    "\n",
    "    for q in tqdm(ground_truth):\n",
    "        doc_id = q['document']\n",
    "        results = search_function(q)\n",
    "        relevance = [d['id'] == doc_id for d in results]\n",
    "        relevance_total.append(relevance)\n",
    "\n",
    "    return {\n",
    "        'hit_rate': hit_rate(relevance_total),\n",
    "        'mrr': mrr(relevance_total),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "497b5be2-56c0-4435-9c08-d765c1db504b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [02:00<00:00, 38.35it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hit_rate': 0.9234925437648585, 'mrr': 0.8481665586052878}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(ground_truth, question_hybrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7016f3b9-872a-48e2-8cbb-56bfd605f7e4",
   "metadata": {},
   "source": [
    "ES knn on questions: `{'hit_rate': 0.773071104387292, 'mrr': 0.6666810748505158}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b6ecc8b0-44ad-4b63-9cea-12150becbb57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_hybrid(q):\n",
    "    question = q['question']\n",
    "    course = q['course']\n",
    "\n",
    "    v_q = model.encode(question)\n",
    "\n",
    "    return elastic_search_hybrid('text_vector', question, v_q, course)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "85d27dc8-0616-4b12-aa95-58d8d9603361",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:51<00:00, 41.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hit_rate': 0.9234925437648585, 'mrr': 0.8461710251422809}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(ground_truth, text_hybrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925e73fa-131f-4ef0-8fe4-fb03f1ef8b25",
   "metadata": {},
   "source": [
    "ES knn on texts: `{'hit_rate': 0.8286146531229739, 'mrr': 0.7062315395144454}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d0687c84-54b0-4b29-af77-dba6e6c51617",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [01:48<00:00, 42.46it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def question_text_hybrid(q):\n",
    "    question = q['question']\n",
    "    course = q['course']\n",
    "\n",
    "    v_q = model.encode(question)\n",
    "\n",
    "    return elastic_search_hybrid('question_text_vector', question, v_q, course)\n",
    "\n",
    "evaluate(ground_truth, question_text_hybrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67782a18-cfac-40c0-8681-5d345ac1c30d",
   "metadata": {},
   "source": [
    "ES knn on questions and answers: `{'hit_rate': 0.9172249837907932, 'mrr': 0.824306606152295}`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ffedb0-3259-46c0-bf75-e955a76a9790",
   "metadata": {},
   "source": [
    "## Reranking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb0e1b5-9ee9-458e-9c3f-f06c5916103c",
   "metadata": {},
   "source": [
    "To use the Reciprocal rank fusion (RRF) score we need to pull the docker image with a more recent version of Elasticsearch:\n",
    "```bash\n",
    "docker run -it \\\n",
    "    --rm \\\n",
    "    --name elasticsearch \\\n",
    "    -m 4GB \\\n",
    "    -p 9200:9200 \\\n",
    "    -p 9300:9300 \\\n",
    "    -e \"discovery.type=single-node\" \\\n",
    "    -e \"xpack.security.enabled=false\" \\\n",
    "    docker.elastic.co/elasticsearch/elasticsearch:8.9.0\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d878d5a-b15a-4459-a6f0-5174af6d1bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search_hybrid_rrf(field, query, vector, course):\n",
    "    knn_query = {\n",
    "        \"field\": field,\n",
    "        \"query_vector\": vector,\n",
    "        \"k\": 5,\n",
    "        \"num_candidates\": 10000,\n",
    "        \"boost\": 0.5,\n",
    "        \"filter\": {\n",
    "            \"term\": {\n",
    "                \"course\": course\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    keyword_query = {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": query,\n",
    "                    \"fields\": [\"question\", \"text\", \"section\"],\n",
    "                    \"type\": \"best_fields\",\n",
    "                    \"boost\": 0.5,\n",
    "                }\n",
    "            },\n",
    "            \"filter\": {\n",
    "                \"term\": {\n",
    "                    \"course\": course\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    search_query = {\n",
    "        \"knn\": knn_query,\n",
    "        \"query\": keyword_query,\n",
    "        \"size\": 5,\n",
    "        \"rank\": {\n",
    "            \"rrf\": {}\n",
    "        },\n",
    "        \"_source\": [\"text\", \"section\", \"question\", \"course\", \"id\"]\n",
    "    }\n",
    "\n",
    "    es_results = es_client.search(\n",
    "        index=index_name,\n",
    "        body=search_query\n",
    "    )\n",
    "    \n",
    "    result_docs = []\n",
    "    \n",
    "    for hit in es_results['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "\n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78c650ec-6e76-4838-a2b0-2902bfd7c922",
   "metadata": {},
   "outputs": [],
   "source": [
    "course = \"data-engineering-zoomcamp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc9dfdaf-c10e-4470-8687-543a566a222f",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I just discovered the course. Can I still join it?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a13b975b-d21b-4ad9-9756-7771319bec83",
   "metadata": {},
   "outputs": [],
   "source": [
    "v_q = model.encode(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce90bf90-8453-491e-8d9e-eee20c1c42b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic_search_hybrid_rrf('question_text_vector', query, v_q, course)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8265dd67-a9c5-4e0b-9c45-9b7326fc4b74",
   "metadata": {},
   "source": [
    "By default, RRF isn't available in a free-tier subscription. But you can try to use 30-day trial or upgrade the subscription plan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a02f804-1d54-45f5-8351-eadb194daa38",
   "metadata": {},
   "source": [
    "### RRF implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "342a7a58-890f-4439-9fb2-a9037185058a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_rrf(rank, k=60):\n",
    "    \"\"\" Our own implementation of the relevance score \"\"\"\n",
    "    return 1 / (k + rank)\n",
    "\n",
    "def elastic_search_hybrid_rrf(field, query, vector, course, k=60):\n",
    "    knn_query = {\n",
    "        \"field\": field,\n",
    "        \"query_vector\": vector,\n",
    "        \"k\": 10,\n",
    "        \"num_candidates\": 10000,\n",
    "        \"boost\": 0.5,\n",
    "        \"filter\": {\n",
    "            \"term\": {\n",
    "                \"course\": course\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    keyword_query = {\n",
    "        \"bool\": {\n",
    "            \"must\": {\n",
    "                \"multi_match\": {\n",
    "                    \"query\": query,\n",
    "                    \"fields\": [\"question\", \"text\", \"section\"],\n",
    "                    \"type\": \"best_fields\",\n",
    "                    \"boost\": 0.5,\n",
    "                }\n",
    "            },\n",
    "            \"filter\": {\n",
    "                \"term\": {\n",
    "                    \"course\": course\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    knn_results = es_client.search(\n",
    "        index=index_name, \n",
    "        body={\n",
    "            \"knn\": knn_query, \n",
    "            \"size\": 10\n",
    "        }\n",
    "    )['hits']['hits']\n",
    "    \n",
    "    keyword_results = es_client.search(\n",
    "        index=index_name, \n",
    "        body={\n",
    "            \"query\": keyword_query, \n",
    "            \"size\": 10\n",
    "        }\n",
    "    )['hits']['hits']\n",
    "    \n",
    "    rrf_scores = {}\n",
    "    # Calculate RRF using vector search results\n",
    "    for rank, hit in enumerate(knn_results):\n",
    "        doc_id = hit['_id']\n",
    "        rrf_scores[doc_id] = compute_rrf(rank + 1, k)\n",
    "\n",
    "    # Adding keyword search result scores\n",
    "    for rank, hit in enumerate(keyword_results):\n",
    "        doc_id = hit['_id']\n",
    "        if doc_id in rrf_scores:\n",
    "            rrf_scores[doc_id] += compute_rrf(rank + 1, k)\n",
    "        else:\n",
    "            rrf_scores[doc_id] = compute_rrf(rank + 1, k)\n",
    "\n",
    "    # Sort RRF scores in descending order\n",
    "    reranked_docs = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    # Get top-K documents by the score\n",
    "    final_results = []\n",
    "    for doc_id, score in reranked_docs[:5]:\n",
    "        doc = es_client.get(index=index_name, id=doc_id)\n",
    "        final_results.append(doc['_source'])\n",
    "    \n",
    "    return final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ac3a817b-0c7a-4e48-8a6f-2a9a2c67a214",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4627/4627 [03:08<00:00, 24.49it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hit_rate': 0.9520207477847418, 'mrr': 0.8745911677833017}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def question_text_hybrid_rrf(q):\n",
    "    question = q['question']\n",
    "    course = q['course']\n",
    "\n",
    "    v_q = model.encode(question)\n",
    "\n",
    "    return elastic_search_hybrid_rrf('question_text_vector', question, v_q, course)\n",
    "\n",
    "evaluate(ground_truth, question_text_hybrid_rrf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a1c0de-8d15-4cc0-b777-b0590e16f3ef",
   "metadata": {},
   "source": [
    "ES hybrid search scores: `{'hit_rate': 0.9250054030689432, 'mrr': 0.8506231539514445}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eefbc39-fe46-42f7-9507-36e9b3cd897e",
   "metadata": {},
   "outputs": [],
   "source": []
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